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Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors

Dan Sha, Zhaohui Jin, Jan Budczies, Klaus Kluck, Albrecht Stenzinger and Frank A. Sinicrope
Dan Sha
1Departments of Medicine and Gastrointestinal Research Unit, Mayo Clinic, Rochester, Minnesota.
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Zhaohui Jin
2Department of Oncology, Mayo Clinic, Rochester, Minnesota.
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  • ORCID record for Zhaohui Jin
Jan Budczies
3Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
4German Cancer Consortium (DKTK), Heidelberg Partner Site, Heidelberg, Germany.
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Klaus Kluck
3Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
4German Cancer Consortium (DKTK), Heidelberg Partner Site, Heidelberg, Germany.
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Albrecht Stenzinger
3Institute of Pathology, University Hospital Heidelberg, Heidelberg, Germany.
4German Cancer Consortium (DKTK), Heidelberg Partner Site, Heidelberg, Germany.
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Frank A. Sinicrope
1Departments of Medicine and Gastrointestinal Research Unit, Mayo Clinic, Rochester, Minnesota.
2Department of Oncology, Mayo Clinic, Rochester, Minnesota.
5Mayo Clinic Comprehensive Cancer Center, Rochester, Minnesota.
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  • For correspondence: sinicrope.frank@mayo.edu
DOI: 10.1158/2159-8290.CD-20-0522 Published December 2020
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Abstract

Tumor mutational burden (TMB), defined as the number of somatic mutations per megabase of interrogated genomic sequence, varies across malignancies. Panel sequencing–based estimates of TMB have largely replaced whole-exome sequencing–derived TMB in the clinic. Retrospective evidence suggests that TMB can predict the efficacy of immune checkpoint inhibitors, and data from KEYNOTE-158 led to the recent FDA approval of pembrolizumab for the TMB-high tumor subgroup. Unmet needs include prospective validation of TMB cutoffs in relationship to tumor type and patient outcomes. Furthermore, standardization and harmonization of TMB measurement across test platforms are important to the successful implementation of TMB in clinical practice.

Significance: Evaluation of TMB as a predictive biomarker creates the need to harmonize panel-based TMB estimation and standardize its reporting. TMB can improve the predictive accuracy for immunotherapy outcomes, and has the potential to expand the candidate pool of patients for treatment with immune checkpoint inhibitors.

Footnotes

  • Note: Supplementary data for this article are available at Cancer Discovery Online (http://cancerdiscovery.aacrjournals.org/).

  • Cancer Discov 2020;10:1808–25

  • Received April 27, 2020.
  • Revision received July 3, 2020.
  • Accepted September 9, 2020.
  • Published first November 2, 2020.
  • ©2020 American Association for Cancer Research.
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Cancer Discovery: 10 (12)
December 2020
Volume 10, Issue 12
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Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors
Dan Sha, Zhaohui Jin, Jan Budczies, Klaus Kluck, Albrecht Stenzinger and Frank A. Sinicrope
Cancer Discov December 1 2020 (10) (12) 1808-1825; DOI: 10.1158/2159-8290.CD-20-0522

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Tumor Mutational Burden as a Predictive Biomarker in Solid Tumors
Dan Sha, Zhaohui Jin, Jan Budczies, Klaus Kluck, Albrecht Stenzinger and Frank A. Sinicrope
Cancer Discov December 1 2020 (10) (12) 1808-1825; DOI: 10.1158/2159-8290.CD-20-0522
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  • Article
    • Abstract
    • Introduction
    • Challenges in TMB Definition and Measurement
    • Variability in TMB Across Solid Tumors
    • TMB as a Predictive Biomarker for Cancer Immunotherapy
    • TMB and Other Biomarkers
    • TMB and DNA Damage Response and Repair
    • Strategies to Optimize TMB as a Predictive Biomarker
    • Conclusions and Future Perspectives
    • Disclosure of Potential Conflicts of Interest
    • Acknowledgments
    • Footnotes
    • References
  • Figures & Data
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